Purchasing Bakery Goods during COVID-19: A Mind Genomics Cartography of Hungarian Consumers - MDPI
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agronomy
Article
Purchasing Bakery Goods during COVID-19: A Mind
Genomics Cartography of Hungarian Consumers
Barbara Biró and Attila Gere *
Department of Postharvest, Supply Chain, Commerce and Sensory Science, Institute of Food Science and
Technology, Hungarian University of Agriculture and Life Sciences, H-1118 Budapest, Hungary;
barbarabirophd@gmail.com
* Correspondence: gereattilaphd@gmail.com
Abstract: At both global and national levels, COVID-19 caused huge changes both in politics and
economics, including the agricultural sector and the food industry, from producers, manufacturers,
and traders to consumers. Since March 2020, many restrictions and protective measures were
introduced worldwide, which only began to be relaxed in the last weeks of spring 2021 as the number
of people vaccinated against the coronavirus increased in Hungary. The aim of this study was
to investigate the attitudes of Hungarian consumers toward food purchases during the COVID-
19 pandemic, in terms of safety. The research was based on the purchase of bakery products,
which are basic food products and are most often found in an unpackaged form in Hungarian
stores. The BimiLeap® study, a revolutionary tool for uncovering people’s minds, was completed
by 125 participants, gathered by a snowballing technique. There were no significant differences
among consumers’ attitudes based on the traditional socio-demographic descriptors; however, the
mindset-based classification was able to differentiate significantly. The three identified mindsets
covered people who themselves consider bakery products, the purchase method, and being in the
Citation: Biró, B.; Gere, A. store as the highest risk of a potential COVID infection.
Purchasing Bakery Goods during
COVID-19: A Mind Genomics Keywords: Mind Genomics; mindsets; COVID-19; bakery products; food purchase
Cartography of Hungarian
Consumers. Agronomy 2021, 11, 1645.
https://doi.org/10.3390/
agronomy11081645
1. Introduction
Academic Editor: Gabrijel Ondrasek The pandemic caused by a new type of coronavirus (SARS-CoV2) has reshaped the
world. At both global and national levels, it has caused huge changes both in politics
Received: 30 June 2021 and economics, including the agricultural sector and the food industry, from producers,
Accepted: 16 August 2021 manufacturers, and traders to consumers. It is questionable which of the changes resulting
Published: 18 August 2021 from the disease control measures introduced will be permanent and to what extent.
In Hungary, a state of emergency was declared by the Government on 11 March 2020,
Publisher’s Note: MDPI stays neutral which led to the introduction of a special legal order [1]. This legal order has already been
with regard to jurisdictional claims in extended several times and, at the time of writing this article, under the Act XL of 2021,
published maps and institutional affil- the Act I of 2021 about the special legal order will expire on the 15th day after the first
iations. day of the autumn session of Parliament [2]. Over the past year, several restrictions and
protective measures have been introduced, such as mandatory wearing of masks in public
places, shops, and public transport; curfews; and the limitation of the number of customers
in shops. In higher education, online education started as soon as the emergency was
Copyright: © 2021 by the authors. declared in 2020, and primary and secondary schools also switched to online education at
Licensee MDPI, Basel, Switzerland. the end of last year. Travel restrictions were also implemented, for both Hungarian and
This article is an open access article foreign citizens [3]. After 30 April 2021, vaccinated citizens are allowed to enter indoor
distributed under the terms and restaurants and entertainment areas, using their vaccination certificate card to enter [4]. As
conditions of the Creative Commons the number of people vaccinated with at least the first dose exceeded 5.5 million, almost
Attribution (CC BY) license (https:// all restrictions have been lifted, including the mandatory mask wearing in stores, public
creativecommons.org/licenses/by/ transport, and public places [5].
4.0/).
Agronomy 2021, 11, 1645. https://doi.org/10.3390/agronomy11081645 https://www.mdpi.com/journal/agronomyAgronomy 2021, 11, 1645 2 of 11
Despite the fact that the restrictions and protective measures were based on the
recommendations by many national and international professional organizations (e.g.,
World Health Organization (WHO)) and have proven to be effective in other countries,
there is skepticism about them among some groups of Hungarian citizens and there is
also a doubt about the vaccines used. Several doctors and health professionals who call
themselves “epidemic critics” have spoken out against the restrictions and vaccination.
They also set up protest pages and groups on social media, the most popular of which
had over 100,000 followers at the time of its shutdown [6]. At the end of February, a
demonstration was also organized against the restrictions and the vaccination [7]. Currently,
the main issues among these groups are the disadvantages of not having a document
proving immunity (vaccination certificate card), questioning the effectiveness of vaccines,
and spreading fake news about their serious side effects.
The pandemic and the restrictions affected every aspect of everyday life, including
shopping. When the restrictions and the protective measures were introduced, consumers
started panic buying and stockpiling, causing shortages of many items such as flour, sugar,
and fresh meat. Customers preferred to visit smaller stores rather than larger super- and
hypermarkets and preferred to pay by card rather than cash [8]. The least purchased
products were clothing, handcrafted products, and unpacked bakery products [9]. Bread
consumption in Hungary had already fallen from 44.5 kg/capita to 34.4 kg between
2010 and 2018 [10], while the popularity of white bread also decreased, from 76% to 61%
between 2007 and 2017 [11]. It is questionable whether the pandemic has exacerbated these
downward trends, as home bread and pastry making has become very popular while the
number of purchases of bakery products has decreased. Based on the work of Sikos and
co-workers, this may be due to the abstention from unpackaged food products [9]. Despite
the downward trend, this is still a large quantity, so the quality of bread and other bakery
products available in stores is a major issue.
In Hungary, bread is most often found in stores in its unpackaged form, which,
together with other unpackaged food products, raises food safety and quality issues. As
ready-to-eat products, bread and bakery goods are usually consumed without reheating.
Packaged breads have been proven to remain microbiologically safe and retain their desired
sensory properties for longer, thus extending their shelf life [12]. Properly handled bread
and bakery goods do not pose a risk due to the high baking temperature and the adequate
moisture content, but the potential for post-process surface contamination is high. Due to
inadequate storage conditions and hygiene, products can be contaminated with various
mold species (e.g., Rhizopus sp., Penicillium sp.) and bacteria (e.g., Bacillus subtilis, Bacillus
licheniformis), which can cause foodborne illnesses [13]. For other foods, ready-to-eat salads
sold on self-service counters and fresh, unpacked fruit and vegetables can pose a food safety
risk due to their microbial load (e.g., Escherichia coli, Bacillus cereus, Clostridium perfringens).
To prevent these illnesses, it is advisable to educate both store staff and consumers about
the proper storing and handling of these products, and to apply controlling and monitoring
systems in the stores. [14,15]. Despite the fact that the possibility of spreading the new
type of coronavirus through food is not significant [16], increased attention must be paid to
hygiene standards.
Uncovering people’s minds about different topics has always been a critical ques-
tion for researchers. Different surveys [17], focus group interviews [18], or ConJoint
analyses [19] are available to complete such studies. Among these many options, the
ConJoint-based Mind Genomics showed an increasing trend and success in the past decades
thanks to its flexibility and wide range of applications. Mind Genomics was developed
by Dr. Howard Moskowitz and introduced in 2006 [20]. Similar to ConJoint analysis, it
requires a topic, a set of questions coupled with possible answers to create hypothetical
products, services, or short stories, which are rated on a predefined rating scale. It has
been applied in various fields, such as uncovering the mind of consumers regarding meat
analogues [21], people’s reaction to COVID-19 restrictions [22], insect-based foods, [23] or
consumer perception of health loss [24]. In the past few years, Mind Genomics followedAgronomy 2021, 11, 1645 3 of 11
international trends and has been transferred to mobile applications under the name of
BimiLeap® . Mind Genomics (and therefore BimiLeap® ) shares some similarities with Con-
Joint analysis, except that BimiLeap® creates a special experimental design that presents
24 unique vignettes to each participant in an online, platform-independent way.
As mentioned earlier, unpackaged bakery products present some questions regarding
the spread of COVID-19 or other infections. Therefore, we aimed to map consumers’ atti-
tudes towards purchasing food products during the pandemic using BimiLeap® . Through
BimiLeap® we aimed to identify different mindsets based on predefined elements answer-
ing the questions where and how to purchase different types of bakery products under
different restrictions and protective measures.
2. Materials and Methods
2.1. BimiLeap®
In order to uncover the mind of participants, the advanced version of Mind Genomics,
BimiLeap® , was used. BimiLeap® is freely available to create, run, analyze, and disseminate
Mind Genomics studies. [23,25].
The structure of creating a BimiLeap® study is straightforward. In the first step, the
researcher needs to define the topic in question, which in our case was the understanding
of people’s bakery purchasing behaviors during the pandemic. In the next step, four
questions or silos need to be defined, covering the topic as completely as possible to
receive the desired information. These questions are then filled with four elements each,
providing different answers to the questions. Using the elements, BimiLeap® creates so-
called vignettes, which will be later evaluated by the participants. Vignettes are combined
using strictly one element from each silo; however, not all silos are used in order to create
a balanced presentation of elements throughout the study. Vignettes, therefore, can be
comprised of two to four elements. In the presented study, the questions and the elements
were defined by a group of experts after a careful analysis of the bakery market of Hungary
and existing COVID-19 relevant literature (e.g., newspaper articles, Magyar Közlöny,
recommendations of the National Public Health Center (NPCH) and the World Health
Organization (WHO). The created BimiLeap® study is presented in Table 1.
Table 1. Silos (questions) and elements (answers) used in the BimiLeap® study.
Question A: Method of Purchase
A1 I go to the store myself, using public transport
A2 I go to the store myself on foot/by car
A3 I order online
A4 Brought by a friend/family member
Question B: Places of purchase
B1 Hypermarket
B2 Supermarket
B3 Bakery
B4 Convenience store
Question C: Packaging
C1 Product without packaging
C2 Product packaged in the store
C3 Pre-packaged product
C4 Frozen product
Question D: Protective measures
D1 No protective measures in the store
D2 Mandatory wearing of masks and distance keeping in the store
D3 Mandatory disinfection of the hands at the arrival at the store
D4 Limitation of the number of people in the store
Supermarkets: located in both residential and city center areas; floor area: 400–2500 m2 ; number of stock-keeping
units (SKUs): thousands; number of cash registers: between 3–10 units. Hypermarkets: located on the outskirts of
cities; floor area: over 2500 m2 ; number of SKUs: more than 10,000; number of cash registers: more than 10.Agronomy 2021,11,
Agronomy2021, 11,1645
x FOR PEER REVIEW 44 of
of 11
12
Respondentsare
Respondents arethen
thenasked
askedto toraterate
thethe created
created vignettes
vignettes on a on a predefined
predefined 9-point9-point
scale.
scale.
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presented study,study, the following
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question was used,
was used, “How“How safeyou
safe do do feel
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presented bakery-purchasing
bakery-purchasing situation?”,
situation?”, and participants
and participants could provide
could provide their answers
their answers from 1
fromsafe
(not 1 (not safe
at all) toat all) to Each
9 (safe). 9 (safe). Each respondent
respondent sees and sees
ratesand rates 24 vignettes.
24 different different vignettes.
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So, people,
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system thecreates
system100 × 24100
creates × 24 or
or 2400 2400 DIFFERENT
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The structure of
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each of the 16 answers
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each vignette)
for eachis
stored in aistable
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inisa available
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download onceis closed
the study[26].is closed [26].
BimiLeap®® includes further classification questions, such as age
BimiLeap age and
and gender
gender (male,
(male,
female,
female, non-binary) as as well
wellas asan
anadditional
additionalquestion
question defined
defined bybythethe authors.
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In the the
current study,
rent study, it regarded
it regarded thethe place
place of residence
of residence (capital
(capital city,city, city,
city, town,
town, andand village),
village), at
at the
the beginning of the questionnaire. At the end of the study, a non-mandatory,
beginning of the questionnaire. At the end of the study, a non-mandatory, open-ended open-ended
question
question (“What is is your
youropinion
opinionononthethe restrictive/protective
restrictive/protective measures
measures currently
currently in place?”)
in place?”) was
was asked.
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The finished
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shared online
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presented by Figure
sented by Figure 1 (mobile
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Participants see theseenumber
the number of theofvignette
the vignette
in the in the
upper
upper left corner, a short description of the task, the rating questions,
left corner, a short description of the task, the rating questions, and the elements that and the elements
that should
should be rated.
be rated. At At
thethe bottom
bottom of ofthethe screen,the
screen, therating
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presented withwith thethe two
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labels of the endpoints. As soon as the participant clicks on an answer,
labels of the endpoints. As soon as the participant clicks on an answer, the next vignette the next vignette is
immediately presented and there is no possibility to turn back to any
is immediately presented and there is no possibility to turn back to any previous vignettes. previous vignettes.
Figure 1. Example vignette used in the study.
Figure 1. Example vignette used in the study.Agronomy 2021, 11, 1645 5 of 11
2.2. Participants
The study was available between 6–26 April 2021 and altogether 125 participants
successfully filled out the online test. In addition to these 125 participants, a further
72 opened but did not complete the study; therefore, their results were automatically
removed. The age of participants ranged between 19 and 65 years with an average of
28.65 ± 9.70 years. Women were overrepresented in the study, as we aimed to choose
respondents who do regular shopping of bakery products; therefore, 100 females and
25 males participated (80% vs. 20%, respectively). No participant chose the non-binary
option. Participants lived in Budapest (capital of Hungary) (48%), in cities (16%), towns
(21%), and villages (15%).
2.3. Data Analysis
BimiLeap® uses ordinary least squares (OLS) regression to assess the effect of the
elements on the ratings of the participants. The input data matrix has the vignettes
presented to the participants (24 per participant) in its rows and the elements (16 elements)
in its columns plus the rating given by the participants. The variables are coded by 0’s
and 1’s, depending on if the given element was presented or not in the given vignette. The
given rating is also registered and stored in the variable “Rating”. In order to identify the
most influencing variables, Rating is transformed by BimiLeap® . First, ratings of 1–6 on
the scale are transformed to 0 (i.e., low/weak feeling), while ratings of 7–9 on the scale are
transformed to 100 (e.g., high/strong feeling), creating the so-called top analysis. Then, a
small random number is added to generate small standard deviation. That lack of variation
would cause the OLS to crash. The OLS is performed on these data, elements (A1-D4) are
used as independent variables, and the binarized ratings are used as dependent variables
(Table 2). In order to be able to evaluate the lower endpoint of the rating scale (not safe
at all), a similar analysis is done on the bottom end of the scale. For these, BimiLeap®
transforms ratings 1–3 to 100 and 4–9 to 0, creating the so-called bottom analysis. With this
step, we can focus on those elements that generated the lowest ratings on the rating scale,
e.g., “not safe at all”.
Table 2. First six rows of the input matrix for OLS regression.
Binarized
A1 A2 A3 A4 B1 B2 B3 B4 C1 C2 C3 C4 D1 D2 D3 D4 Rating
Rating
1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 9 100.7249753
1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 5 0.427754649
0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 9 100.461673
0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 7 100.1592801
1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 5 0.692818016
0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 6 0.5522144
OLS is run on an individual level, meaning that a separate OLS is run on each of
the 24 rows of the input matrix, as each participant rated 24 vignettes. OLS generates
regression coefficients, which are stored; therefore, each participant receives a vector of
17 values, one for the additive constant and 16 coefficients for the 16 elements (dependent
variables). As regression coefficients describe the relationship between a predictor variable
and the response, the extent of the obtained coefficients tells us the effect of the given
element on the rating given by the participants.
Participants are then clustered using k-means clustering using these regression co-
efficients and Pearson 1-R distance to create similarly thinking clusters, the so-called
mindsets [27]. As the mathematical formula tells us, BimiLeap® does not use a priori
segmentation; rather, the pattern of the coefficients helps us to group respondents to create
mindsets’ thinking as similar as possible. By default, BimiLeap® clustered the respondentsAgronomy 2021, 11, 1645 6 of 11
into two mindsets and then into three mindsets, because they represent different patterns
of thinking about the same topic. [28]. Although these analyses are done automatically
by BimiLeap® , the presented study was re-analyzed manually to get full control over the
data set. The applied clustering indices suggested that three mindsets should be kept. Data
analysis was done using R-project (version R-3.6.0) and lm.beta package [29].
3. Results and Discussion
3.1. Results of the Total Panel, Gender, and Place of Residence
The questionnaire was completed after the tightening in March 2021 (curfew extension,
closure of stores and schools), before the introduction of the relaxations predicted after
reaching 2.5 million people vaccinated with at least the first dose. Table 3 shows the results
of the total panel, and results disaggregated by gender and by type of residence.
Table 3. Regression coefficients for models relating the presence/absence of the elements to the rating of safety, after binary
transformation. The highest coefficients of each group are colored as gray. High positive coefficients denote a strong feeling
of safety, while low negative coefficients mean the opposite. Coefficients around 0 mean neutral feelings. Superscript letters
denote the homogenous subsets defined by Tukey post hoc test (p < 0.05).
Code Elements Total Male Female Capital City Town Village
City
I go to the store myself,
A1 using public transport −1 −1 −1 −2 −8 4 2
I go to the store myself on
A2 foot/by car 0 1 0 1 −8 3 −2
A3 I order online 0 7 −1 4 −11 4 −1
Brought by a
A4 friend/family member −1 3 −2 3 −14 −8 8
B1 Hypermarket 2 3 3 2 −6 7 6
B2 Supermarket 4 4 4 2 2 7 8
B3 Bakery −1 −6 a 0b −1 −6 1 2
B4 Convenience store 1 0 1 −2 4 3 3
Product without
C1 packaging 4 6 3 7 8 −1 −3
Product packaged in the
C2 store 6 9 5 8 6 2 2
C3 Pre-packaged product 2 5 2 5 5 1 −5
C4 Frozen product −3 5 −4 1 0 −4 −13
No protective measures in
D1 the store −4 −6 −3 2 −6 −11 −8
Mandatory wearing of
masks and distance
D2 keeping in the store −2 0 −2 1 −3 0 −11
Mandatory disinfection of
the hands at the arrival at
D3 the store −4 −9 −3 −2 −2 −4 −10
Limitation of the number
D4 of people in the store −3 0 −4 0 −10 −4 −3
Since the coefficients and their differences were too small, it was not possible to draw
any firm conclusions from the results of the total panel. In general, locally packaged
products were considered the safest. The respondents were skeptical about the restrictive
measures, none of which were considered really safe. Of the types of stores, they were
most confident in the safety of hypermarkets and supermarkets.
Comparing women and men, the coefficients showed that men perceive online shop-
ping to be much safer than women, but among shopping locations, hyper- and supermar-
kets were considered to be equally safe. While men considered all types of packaging as
safe, women were more doubtful about frozen products. The only significant differenceAgronomy 2021, 11, 1645 7 of 11
between men and women was found in the case of purchasing in a bakery, which was
considered less safe by men.
Residents of the capital city considered online shopping to be the safest, and they
trusted unpacked and in-store packaged bakery products the most. The latter was also
true for people living in cities, but they thought that shopping in a small shop was the
safest. Those living in towns thought that ordering online and using public transport to
get to the shop were the safest and had the greatest trust in shopping in hypermarkets and
supermarkets. People living in villages also thought that hypermarkets and supermarkets
were the safest places to shop and having a friend or family member do the shopping and
deliver the product to their homes was the safest.
Looking at the results of the total panel, respondents did not consider any of the listed
protective measures to be safe and had less trust in bakeries and frozen products.
Men did not consider it safe to buy bakery products from a bakery, and did not
consider the lack of protective measures or the mandatory disinfection of their hands to be
safe. Women did not consider either measure to be particularly safe and, unlike men, they
did not trust frozen products.
Respondents living in the capital were generally neutral about almost every element:
The small coefficients suggest that they did not consider them safe or unsafe. City residents,
on the other hand, did not consider any way of purchasing bakery products to be safe,
especially if the products were taken home by a friend or family member. They also did
not consider hypermarkets and bakeries to be safe, and they had a lack of trust about the
limitation of the number of customers in stores. People living in towns also considered to
be the least safe if they did not buy the product themselves, and if there were no protective
measures in force. Villagers completely rejected frozen products and did not consider any
of the protective measures to be safe.
3.2. Identifying Mindsets
The emergent mindsets showed three distinct groups (Table 4). Mindset 1 appeared to
have more trust in supermarkets, mostly when they go shopping themselves, Mindset 2
appeared to have more trust in the protective measures in the stores and products packaged
in-store, whereas Mindset 3 appeared to have more trust in every type of the products,
especially if they order them online. The three mindsets showed completely different
pictures about what they consider safe:
Table 4. Regression coefficients for the top scores of the three mindsets. High positive coefficients mean that respondents did
feel the element safer. The highest coefficients of each group are colored as gray. Superscript letters denote the homogenous
subsets defined by Tukey post hoc test (p < 0.05).
Mind-Set 1 Mind-Set 2 Mind-Set 3
Code Elements
(n = 27) (n = 53) (n = 45)
A1 I go to the store myself, using public transport 15 b −15 a 5b
A2 I go to the store myself on foot/by car 17 b −13 a 6b
A3 I order online 8b −10 a 10 b
A4 Brought by a friend/family member 9b −12 a 7b
B1 Hypermarket 0 1 5
B2 Supermarket 12 −2 6
B3 Bakery 3 −3 −1
B4 Convenience store 0 0 2
C1 Product without packaging −13 a 3b 15 b
C2 Product packaged in the store −7 a 8b 11 b
C3 Pre-packaged product −17 a 6b 10 b
C4 Frozen product −17 a −4 b 7b
D1 No protective measures in the store −3 b 9b −19 a
D2 Mandatory wearing of masks and distance keeping in the store 1b 4b −12 a
D3 Mandatory disinfection of the hands at the arrival at the store 3b 6b −21 a
D4 Limitation of the number of people in the store 5b 9b −22 aAgronomy 2021, 11, 1645 8 of 11
Mindset 1:
I go to the store myself on foot/by car
Supermarket
Product packaged in the store
Limitation of the number of people in the store
Mindset 2:
I order online
Hypermarket
Product packaged in the store
No protective measures in the store or limitation of the number of people in the store
Mindset 3:
I order online
Supermarket
Product without packaging
Mandatory wearing of masks and distance keeping in the store
As the final part of the questionnaire, respondents were asked an optional open-ended
question, which was “What is your opinion on the restrictive/protective measures currently
in place?”. The obtained results were supported by the short text responses to this question,
some of which are shown in Table 5. Based on the comments, we concluded that people
would have not supported the relaxations at the time of the completion of the study.
Table 5. The respondents’ comments on the restrictive and protective measures, recorded at the end
of the questionnaire.
“What is your Opinion on the Restrictive/Protective Measures
Mind-Sets
Currently in Place?”
Tightening for bakery products is required.
MS-1 Hungarian people have a bad habit of touching all non-packaged bakery
products, fruits, and vegetables, so the restrictions and protective measures
do not make much sense.
They are pointless, except for hand disinfection.
It is better because there is no crowd. However, the mandatory 1.5 m
distance is no longer considered by most of the people.
MS-2
I think the measures used in stores are good. The limitation of the number
of the customers, the mandatory mask-wearing, and hand disinfection
make me feel safer, but at the same time, public transport is still crowded.
I agree with them basically. There are some things on which I would
tighten up (e.g., smoking, eating on the streets) and I would like to see
increased police presence.
I find the limitation of the number of customers a little unrealistic, since a
lot of people can gather outside the stores, and they will be much closer to
each other than inside. Of course, it is the large shops and malls that are
MS-3
the most important in this case.
The measures are not feasible in the stores, and the stores do not comply
with the regulations.
It does not really matter, because it just slows down the spreading of the
virus. Obviously, it is better than not having any measures, but the
effective method would be mandatory vaccination.
3.3. Response Time
BimiLeap® enables the researchers to conduct an evaluation of the response times of
the participants. Response times are measured similarly to the ratings, e.g., each vignette
receives a response time value, which is the time in seconds from the presentation of the
vignette to the statement of the response (e.g., clicking on the rating). As these values have
significant information content, the response times were also extracted and are presented
in Table 6. Higher results mean a longer time needed to answer a vignette that containedAgronomy 2021, 11, 1645 9 of 11
the given element. Elements that are selected quickly are the main factors in the creation of
a “gut response”, which is a reaction to a situation based on a person’s instinct and feelings,
rather than on a logical analysis. The highest values for total panel were registered for
elements “Convenience store” and “Frozen product”, meaning that participants required
the longest time (e.g., it required a higher cognitive load) to answer vignettes containing
these elements. The shortest times were recorded for the protective measures, meaning that
these elements were easy to answer, and participants did not need a long time to formulate
their ratings.
Table 6. Regression coefficients for response times of total panel and the three mindsets. Higher numbers mean slower
reactions. The highest numbers of each group are colored as dark gray, while the lowest are colored by light gray.
Mind-Set 1 Mind-Set 2 Mind-Set 3
Code Additive Constant Total
(n = 27) (n = 53) (n = 45)
A1 I go to the store myself, using public transport 1.4 1.4 1.5 1.4
A2 I go to the store myself on foot/by car 1.5 1.5 1.4 1.6
A3 I order online 1.5 1.5 1.5 1.5
A4 Brought by a friend/family member 1.6 1.7 1.4 1.6
B1 Hypermarket 1.6 1.8 1.4 1.8
B2 Supermarket 1.5 1.5 1.4 1.6
B3 Bakery 1.6 1.5 1.6 1.7
B4 Convenience store 1.7 1.9 1.6 1.6
C1 Product without packaging 1.4 1.2 1.7 1.3
C2 Product packaged in the store 1.5 1.1 1.6 1.5
C3 Pre-packaged product 1.5 1.5 1.6 1.5
C4 Frozen product 1.7 1.4 1.8 1.8
D1 No protection measures in the store 1.4 1.6 1.4 1.1
D2 Mandatory wearing of masks and distance keeping in the store 1.4 1.8 1.4 1.2
D3 Mandatory disinfection of the hands at the arrival at the store 1.4 1.4 1.4 1.4
D4 Limitation of the number of people in the store 1.4 1.7 1.4 1.3
Regarding mindsets, Mindset 1 had the shortest response times all related to pack-
aging (“Product packaged in the store” and “Product without packaging”, 1.1 and 1.2,
respectively). Members of Mindset 1 rated these elements the fastest, while the longest time
was needed to answer vignettes containing the elements “Hypermarket” or “Mandatory
wearing of masks and distance keeping in the store”.
Mindset 2 needed the longest times to rate the vignettes. Generally, the elements
performed similarly (coefficients around 1.4) but “Product without packaging” and “Frozen
product” were rated more slowly (1.8) compared to the other elements.
The highest range in the response times was observed for Mindset 3, where the
fastest ratings were registered when elements “No protection measures in the store” or
“Mandatory wearing of masks and distance keeping in the store” were presented (1.1 and
1.2, respectively), while “Hypermarket” and “Frozen product” required the longest time
to answer.
From these results, the sharpest differences were observed between Mindsets 1 and 3,
while Mindset 2 served as an in-between segment. These sharp differences were expressed
mainly with elements about protective measures, which needed longer response times
from Mindset 1 than 2.
4. Conclusions
Restrictions due to COVID-19 have been in force since March 2020 in Hungary, and
the study was run at the peak of the pandemic’s third wave. The obtained data showed
that shopping routines have changed. Although many risk factors play a role in the
transmission of the virus, different consumer groups can be defined based on what they
consider the most unsafe. Such information can be used (1) to understand people’s motives
and (2) to define protective measures. In the presented study, we analyzed bakery goods,Agronomy 2021, 11, 1645 10 of 11
but these results can be transferred to other goods as well, as the introduced methodology
enables us to do so.
There were no significant differences among consumers’ attitudes based on the tradi-
tional socio-demographic descriptors, which did not show significant differences among
consumers; however, the mindset-based classification was able to identify the most discrim-
inating elements. The three identified mindsets covered people who considered bakery
products themselves as the highest risk (Mindset 1), people who considered the purchase
method as the highest risk (Mindset 2), and people who considered being in the store as
the highest risk of a potential infection (Mindset 3).
Further studies should be made to uncover how these events and restrictions have
affected consumer behavior, in particular, how these patterns persist after the pandemic is
over. To our knowledge, there has been no similar study conducted in the international
literature. Comparisons among different cultures might give different results, since, in
many countries, bakery products are sold as packaged products, which raises fewer food
safety issues compared to the ones without any packaging.
Author Contributions: Conceptualization: B.B. and A.G.; methodology: B.B. and A.G.; software: B.B.
and A.G.; validation: A.G.; formal analysis: B.B. and A.G.; writing—original draft preparation: B.B.
and A.G.; writing—review and editing: B.B. and A.G.; funding acquisition: B.B. and A.G. All authors
have read and agreed to the published version of the manuscript.
Funding: Supported by the ÚNKP-20-3-II-SZIE-23 New National Excellence Program of the Ministry
for Innovation and Technology from the source of the National Research, Development and Inno-
vation Fund. The Project was supported by the European Union and co-financed by the European
Social Fund (grant agreement no. EFOP-3.6.3-VEKOP-16-2017-00005).
Institutional Review Board Statement: Ethical review and approval were waived for this study as
research data has been robustly anonymized, such that the original providers of the data cannot be
identified, directly or indirectly, by anyone.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The data presented in this study are available on request from the
corresponding author.
Acknowledgments: B.B. thanks the support of the Doctoral School of Food Sciences, Hungarian
University of Agriculture and Life Sciences. A.G. thanks the support of the Premium Postdoctoral
Research Program of the Hungarian Academy of Sciences and the support of National Research,
Development and Innovation Office of Hungary (OTKA, contracts No. K134260 and FK137577).
Conflicts of Interest: The authors declare no conflict of interest.
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